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id="page-header" style="background-image: url('https://gitee.com/wrj1006er/pic/raw/master/pic/7cdad2bc0a575ec813f2ffbdcd4afb84d914fd26.jpg@942w_531h_progressive.webp')"><nav id="nav"><span id="blog_name"><a id="site-name" href="/">1006er的博客</a></span><div id="menus"><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> 首页</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> 归档</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> 标签</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> 分类</span></a></div><div class="menus_item"><a class="site-page" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> 清单</span><i class="fas fa-chevron-down expand"></i></a><ul class="menus_item_child"><li><a class="site-page" href="/music/"><i class="fa-fw fas fa-music"></i><span> Music</span></a></li><li><a class="site-page" href="/movies/"><i class="fa-fw fas fa-video"></i><span> Movie</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> 友情链接</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> 关于</span></a></div></div><div id="toggle-menu"><a class="site-page"><i class="fas fa-bars fa-fw"></i></a></div></div></nav><div id="post-info"><h1 class="post-title">联邦学习（1）</h1><div id="post-meta"><div class="meta-firstline"><span class="post-meta-date"><i class="far fa-calendar-alt fa-fw post-meta-icon"></i><span class="post-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-10-26T16:12:03.000Z" title="发表于 2021-10-27 00:12:03">2021-10-27</time><span class="post-meta-separator">|</span><i class="fas fa-history fa-fw post-meta-icon"></i><span class="post-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-10-31T06:17:18.465Z" title="更新于 2021-10-31 14:17:18">2021-10-31</time></span><span class="post-meta-categories"><span class="post-meta-separator">|</span><i class="fas fa-inbox fa-fw post-meta-icon"></i><a class="post-meta-categories" href="/categories/%E8%81%94%E9%82%A6%E5%AD%A6%E4%B9%A0/">联邦学习</a></span></div><div class="meta-secondline"><span class="post-meta-separator">|</span><span class="post-meta-pv-cv"><i class="far fa-eye fa-fw post-meta-icon"></i><span class="post-meta-label">阅读量:</span><span id="busuanzi_value_page_pv"></span></span></div></div></div></header><main class="layout" id="content-inner"><div id="post"><article class="post-content" id="article-container"><h1 id="联邦学习论文笔记"><a href="#联邦学习论文笔记" class="headerlink" title="联邦学习论文笔记"></a>联邦学习论文笔记</h1><h2 id="《Communication-Efficient-Learning-of-Deep-Networks-from-Decentralized-Data》"><a href="#《Communication-Efficient-Learning-of-Deep-Networks-from-Decentralized-Data》" class="headerlink" title="《Communication-Efficient Learning of Deep Networks from Decentralized Data》"></a>《Communication-Efficient Learning of Deep Networks from Decentralized Data》</h2><h3 id="研究背景"><a href="#研究背景" class="headerlink" title="研究背景"></a>研究背景</h3><p>终端设备的数量及其产生的数据都在迅速增加，如果能利用这些数据来训练机器学习(尤其是近年来取得巨大成功的深度学习)模型，服务提供商可以给用户更好的体验。一个例子是，Google利用用户的打字习惯来训练模型，可以在用户输入上文之后给出最有可能的下文，从而提高用户输入速度。与单机的机器学习不同，该场景下的机器学习是分布式的，具有以下特点</p>
<ol>
<li><em>需要保护用户隐私</em> 终端设备的数据可能是高度个性化的，不是所有用户都希望这些数据被上传到数据中心，因此模型的训练应该在不传输原始训练数据的前提下进行。</li>
<li><em>数据分布非独立同分布</em> 由于各个用户的使用习惯等因素的差别，不同终端设备收集到的数据很可能不符合相同的分布，从而一个终端设备的数据无法代表全局的数据。</li>
<li><em>数据不平衡</em> 有的用户是手机的重度使用者，而其他用户则不是，因此不同终端设备收集到的数据量可能会有很大差别。不平衡的数据会影响模型训练结果的好坏。</li>
<li><em>通信受限</em> 终端设备的网络连接是不稳定的，并且通信成本高、速度慢等特点进一步限制了数据的传输。很少有人愿意用流量来传输十几个GB的数据。</li>
</ol>
<h3 id="算法的提出"><a href="#算法的提出" class="headerlink" title="算法的提出"></a>算法的提出</h3><p>单机的机器学习无法解决这些问题。一种可行的方法是，每个拥有数据的终端设备利用自己的数据训练局部模型，在训练过程中不同的设备之间相互通信，所有局部模型借助通信整合到一起形成一个全局模型，该全局模型仿佛是收集了所有数据之后训练得到的模型。这便是联邦学习(Federated Learning)的思想。如何将局部模型整合成为全局模型是联邦学习的关键问题，FedAvg算法正是为此而提出的。</p>
<h3 id="传统的FedSGD方法与FedAvg方法"><a href="#传统的FedSGD方法与FedAvg方法" class="headerlink" title="传统的FedSGD方法与FedAvg方法"></a>传统的FedSGD方法与FedAvg方法</h3><p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/1.png" alt="RUNOOB 图标"></p>
<h4 id="FedSDG"><a href="#FedSDG" class="headerlink" title="FedSDG"></a>FedSDG</h4><ol>
<li>每一次epoch，各个移动端计算梯度信息。</li>
<li>梯度信息传输到服务器，服务器依据梯度信息进行更新。</li>
</ol>
<p>缺点：每一轮次的计算需要中心服务器与客户端进行数据交流</p>
<h4 id="FedAvg"><a href="#FedAvg" class="headerlink" title="FedAvg"></a>FedAvg</h4><ol>
<li><p>先在本地的进行多次的优化。</p>
</li>
<li><p>讲各边缘端的模型传送到服务器。</p>
</li>
<li><p>依据各个边缘端的模型利用求平均的方式代替中心服务器的模型。</p>
<p>联邦学习的目标是经验风险最小化</p>
</li>
</ol>
<p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/2.png" alt="RUNOOB 图标"></p>
<h3 id="FedAvg的伪代码"><a href="#FedAvg的伪代码" class="headerlink" title="FedAvg的伪代码"></a>FedAvg的伪代码</h3><p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/5.png" alt="RUNOOB 图标"></p>
<ol>
<li><p>初始化一个模型</p>
</li>
<li><p>随机选择m个节点</p>
</li>
<li><p>在选取的m个个边缘节点上进行模型训练</p>
</li>
<li><p>m个边缘节点的模型上传中心服务器</p>
</li>
<li><p>求和取平均然后替换中学服务器的节点</p>
<h2 id="《Toward-Resource-Efficient-Federated-Learning-in-Mobile-Edge-Computing》"><a href="#《Toward-Resource-Efficient-Federated-Learning-in-Mobile-Edge-Computing》" class="headerlink" title="《Toward Resource-Efficient Federated Learning in Mobile Edge Computing》"></a>《Toward Resource-Efficient Federated Learning in Mobile Edge Computing》</h2></li>
</ol>
<h3 id="研究背景-1"><a href="#研究背景-1" class="headerlink" title="研究背景"></a>研究背景</h3><p>这篇论文主要讲的是在联邦学习中，资源的高效利用问题，首先联邦学习是一个机器学习框架，能有效帮助多个机构在满足用户隐私保护、数据安全和政府法规的要求下，进行数据使用和机器学习建模。然后论文列举了两个典型的应用场景，虚拟键盘和自动驾驶，以虚拟键盘为例，谷歌的一个ai团队希望提供用户在手机上的输入，判断用户下一个可能的输入，从而加快用户打字的速度，这就存在几个问题</p>
<ol>
<li>   出于隐私保护的原因，用户的输入数据不可以传送到中心服务器上进行训练。</li>
<li>   如果将所有的数据传送到中心服务器上服务器的计算压力会很大</li>
<li>   数据分布非独立同分布。由于各个用户的使用习惯等因素的差别，不同终端设备收集到的数据很可能不符合相同的分布，从而一个终端设备的数据无法代表全局的数据</li>
<li>   通信受限。终端设备的网络连接是不稳定的，并且通信成本高、速度慢等特点进一步限制了数据的传输。很少有人愿意用流量来传输十几个GB的数据。</li>
</ol>
<h3 id="谷歌智能键盘"><a href="#谷歌智能键盘" class="headerlink" title="谷歌智能键盘"></a>谷歌智能键盘</h3><p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/6.png" alt="谷歌智能键盘"></p>
<ol>
<li>   下载中心服务器的全局模型，并且在本机上利用本机自己产生的数据集进行训练（数据的量小，所以不会占用手机的太多的算力）</li>
<li>   将模型更改的部分加密传送到中心服务器上进行训练（减少传送的数据量）</li>
<li>   中心服务器根据各个终端传送的数据，对全局模型进行更新</li>
<li> 移动端下载全局模型</li>
</ol>
<h3 id="联邦学习中资源的问题"><a href="#联邦学习中资源的问题" class="headerlink" title="联邦学习中资源的问题"></a>联邦学习中资源的问题</h3><ol>
<li>   信息交流（上传数据与下载数据）</li>
<li>   移动端的算力资源受限</li>
<li>   能源消耗，（高性能模式与低性能模式）（1.本地计算时的能耗2.上传下载时的能耗）</li>
<li>   数据问题 1.数据的稀缺2.本地数据集之间的非独立同分布</li>
</ol>
<h3 id="传统的方法"><a href="#传统的方法" class="headerlink" title="传统的方法"></a>传统的方法</h3><p>传统的资源利用方法可以大致分为两类</p>
<h4 id="黑盒方法"><a href="#黑盒方法" class="headerlink" title="黑盒方法"></a>黑盒方法</h4><ol>
<li>   设置超参数</li>
<li>   选择合适的移动端（计算能力平均）</li>
<li>   数据补偿（将来自一小部分的数据被全局共享，减少数据的non-IID程度，从而提升模型的性能）（有隐私泄露的风险）</li>
<li>   分层聚合（使模型的聚合具有较小的成本）<h4 id="白盒方法"><a href="#白盒方法" class="headerlink" title="白盒方法"></a>白盒方法</h4></li>
<li>   压缩网络模型</li>
<li>   知识蒸馏（客户端之间共享类分数并汇总以获得模型更新的共识）（模型压缩）</li>
<li>   特征融合（平衡non-IDD数据，减少移动端与中心服务器的数据传输轮次）</li>
<li>   异步更新（网络分为深层与浅层，浅层的更新更加的频繁，而深层的更新次数相对较少）（网络精度异常抖动）</li>
</ol>
<p>注：白盒方法与黑盒方法可以同时使用</p>
<h3 id="基于模块的白盒方法"><a href="#基于模块的白盒方法" class="headerlink" title="基于模块的白盒方法"></a>基于模块的白盒方法</h3><p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/7.png"></p>
<p>利用CNN进行举例</p>
<ol>
<li>将全局模块划分为四个子模块（分别训练不同的filter）</li>
<li>并且中央服务器应该定期交换子模型之间的过滤器块，以确保全局模型的所有过滤器将被所有客户端平等训练。</li>
<li>在聚合全局模型时，中央服务器会先重构全局模型的所有副本，然后在副本之间进行梯度聚合。</li>
</ol>
<h3 id="启发式搜索算法"><a href="#启发式搜索算法" class="headerlink" title="启发式搜索算法"></a>启发式搜索算法</h3><p>三级递归搜索的启发式算法</p>
<ol>
<li>   子模型结构搜索：对于每个模型分区，将其子模型按大小从大到小排序，客户端按通道条件从好到坏排序。 将客户与子模型按顺序匹配。 调用程序 S2 得到次优的能量和时间成本，然后是次优的效用。比较所有次优的效用以获得最优的效用。</li>
<li>   时长搜索：给定时间成本的搜索范围，采用二分搜索技术。 在每一轮搜索中，调用程序S3得到次优的计算频率和传输速率。 计算能源成本并将其与预算进行比较。 二分搜索一直持续到能源成本足够接近预算。 返回次优的能源和时间成本。</li>
<li>   资源配置搜索：使用输入时间成本和模型划分，对于所有客户端，以最小的能量成本计算次优的计算频率和传输速率。 返回次优的计算频率、传输速率和能源成本。</li>
</ol>
<p>算法的时间复杂度 O(Jlog2J + Nlog2N + Jlog2T)</p>
<h3 id="实验结果"><a href="#实验结果" class="headerlink" title="实验结果"></a>实验结果</h3><p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/8.png"><br>注解：<br>MFL(基于模块的联邦学习) TFL(传统联邦学习)</p>
<p>LP low power HP heigh power：移动端能耗</p>
<p>IID non-IID：数据的分布</p>
<p>衰落信道：模拟无线情况</p>
</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta">文章作者: </span><span class="post-copyright-info"><a href="mailto:undefined">wrj</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta">文章链接: </span><span class="post-copyright-info"><a href="http://example.com/2021/10/27/%E8%81%94%E9%82%A6%E5%AD%A6%E4%B9%A0%EF%BC%881%EF%BC%89/">http://example.com/2021/10/27/%E8%81%94%E9%82%A6%E5%AD%A6%E4%B9%A0%EF%BC%881%EF%BC%89/</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta">版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="http://example.com" target="_blank">1006er的博客</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"><a class="post-meta__tags" href="/tags/%E8%AE%BA%E6%96%87/">论文</a></div><div class="post_share"><div 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card-info"><div class="card-info-avatar is-center"><img class="avatar-img" src="/img/avatar.png" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/><div class="author-info__name">wrj</div><div class="author-info__description"></div></div><div class="card-info-data"><div class="card-info-data-item is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">46</div></a></div><div class="card-info-data-item is-center"><a href="/tags/"><div class="headline">标签</div><div class="length-num">19</div></a></div><div class="card-info-data-item is-center"><a href="/categories/"><div class="headline">分类</div><div class="length-num">7</div></a></div></div><a class="button--animated" id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/xxxxxx"><i class="fab fa-github"></i><span>Follow Me</span></a></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn 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class="toc-text">研究背景</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%AE%97%E6%B3%95%E7%9A%84%E6%8F%90%E5%87%BA"><span class="toc-number">1.1.2.</span> <span class="toc-text">算法的提出</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BC%A0%E7%BB%9F%E7%9A%84FedSGD%E6%96%B9%E6%B3%95%E4%B8%8EFedAvg%E6%96%B9%E6%B3%95"><span class="toc-number">1.1.3.</span> <span class="toc-text">传统的FedSGD方法与FedAvg方法</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#FedSDG"><span class="toc-number">1.1.3.1.</span> <span class="toc-text">FedSDG</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#FedAvg"><span class="toc-number">1.1.3.2.</span> <span class="toc-text">FedAvg</span></a></li></ol></li><li class="toc-item toc-level-3"><a class="toc-link" href="#FedAvg%E7%9A%84%E4%BC%AA%E4%BB%A3%E7%A0%81"><span class="toc-number">1.1.4.</span> <span class="toc-text">FedAvg的伪代码</span></a></li></ol></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E3%80%8AToward-Resource-Efficient-Federated-Learning-in-Mobile-Edge-Computing%E3%80%8B"><span class="toc-number">1.2.</span> <span class="toc-text">《Toward Resource-Efficient Federated Learning in Mobile Edge Computing》</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#%E7%A0%94%E7%A9%B6%E8%83%8C%E6%99%AF-1"><span class="toc-number">1.2.1.</span> <span class="toc-text">研究背景</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E8%B0%B7%E6%AD%8C%E6%99%BA%E8%83%BD%E9%94%AE%E7%9B%98"><span class="toc-number">1.2.2.</span> <span class="toc-text">谷歌智能键盘</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E8%81%94%E9%82%A6%E5%AD%A6%E4%B9%A0%E4%B8%AD%E8%B5%84%E6%BA%90%E7%9A%84%E9%97%AE%E9%A2%98"><span class="toc-number">1.2.3.</span> <span class="toc-text">联邦学习中资源的问题</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E4%BC%A0%E7%BB%9F%E7%9A%84%E6%96%B9%E6%B3%95"><span class="toc-number">1.2.4.</span> <span class="toc-text">传统的方法</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E9%BB%91%E7%9B%92%E6%96%B9%E6%B3%95"><span class="toc-number">1.2.4.1.</span> <span class="toc-text">黑盒方法</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E7%99%BD%E7%9B%92%E6%96%B9%E6%B3%95"><span class="toc-number">1.2.4.2.</span> <span class="toc-text">白盒方法</span></a></li></ol></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%9F%BA%E4%BA%8E%E6%A8%A1%E5%9D%97%E7%9A%84%E7%99%BD%E7%9B%92%E6%96%B9%E6%B3%95"><span class="toc-number">1.2.5.</span> <span class="toc-text">基于模块的白盒方法</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%90%AF%E5%8F%91%E5%BC%8F%E6%90%9C%E7%B4%A2%E7%AE%97%E6%B3%95"><span class="toc-number">1.2.6.</span> <span 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